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<p>This paper deals with the Bayesian safety assessment of autonomous vehicles (AV) using as a key safety measure the probability of catastrophic failure per mile of driving (<i>pfm</i>), assumed a random variable. The paper takes the view that <i>pfm</i> may (and typically will) vary due to changing road driving conditions. Accommodating this variation in a Bayesian inference on <i>pfm</i> requires one to use a multivariate probabilistic model whereby the changeable <i>pfm</i> is captured explicitly for the different driving conditions. The model that we use in this work is derived from our prior work and accounts for the uncertainties in both—the operational profile (i.e., the likelihood of the different driving conditions) and the <i>pfm</i>s, conditional on the respective operating conditions. The concept of the “dynamic AV safety assessment (DyAVSA)” is presented in the work, too, whereby the Bayesian predictions used <i>at run time</i> rely on the operational data collected by a fleet of AVs. DyAVSA benefits both: (1) the AV vendors, for monitoring the safety changes of the entire AV fleet; (2) the owners/users of individual AVs, whose safety assessment is personalized and different from the assessment of the AV fleet. DyAVSA thus offers a <i>major change</i> in the AV safety management than is currently the case. It allows the AV users/owners to benefit from the aggregated safety relevant data collected from a fleet of AVs. Our findings show that the benefits from DyAVSA for the owners/operators of the individual AV instances may be significant: the safety predictions they can make by using the data collected by <i>the entire fleet of</i> AV instances and shared among them, may differ considerably from the predictions the AV instances would be able to make relying on own observations only. Sharing data would lead to a much <i>more rapid reduction of uncertainty</i> in the <i>pfm</i>s than would be the case if the AV instances relied on their own observations only. The presented DyAVSA, based on a multivariate Bayesian safety assessment, can be applied to other complex intelligent systems such as robots, UAVs, etc.</p>

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Dynamic safety assessment of Autonomous Vehicle based on Multivariate Bayesian Inference (DyAVSA)

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  • 发表日期：

    2025年8月1日

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  • Peter Popov

摘要

This paper deals with the Bayesian safety assessment of autonomous vehicles (AV) using as a key safety measure the probability of catastrophic failure per mile of driving (pfm), assumed a random variable. The paper takes the view that pfm may (and typically will) vary due to changing road driving conditions. Accommodating this variation in a Bayesian inference on pfm requires one to use a multivariate probabilistic model whereby the changeable pfm is captured explicitly for the different driving conditions. The model that we use in this work is derived from our prior work and accounts for the uncertainties in both—the operational profile (i.e., the likelihood of the different driving conditions) and the pfms, conditional on the respective operating conditions. The concept of the “dynamic AV safety assessment (DyAVSA)” is presented in the work, too, whereby the Bayesian predictions used at run time rely on the operational data collected by a fleet of AVs. DyAVSA benefits both: (1) the AV vendors, for monitoring the safety changes of the entire AV fleet; (2) the owners/users of individual AVs, whose safety assessment is personalized and different from the assessment of the AV fleet. DyAVSA thus offers a major change in the AV safety management than is currently the case. It allows the AV users/owners to benefit from the aggregated safety relevant data collected from a fleet of AVs. Our findings show that the benefits from DyAVSA for the owners/operators of the individual AV instances may be significant: the safety predictions they can make by using the data collected by the entire fleet of AV instances and shared among them, may differ considerably from the predictions the AV instances would be able to make relying on own observations only. Sharing data would lead to a much more rapid reduction of uncertainty in the pfms than would be the case if the AV instances relied on their own observations only. The presented DyAVSA, based on a multivariate Bayesian safety assessment, can be applied to other complex intelligent systems such as robots, UAVs, etc.